High-risk AI systems in healthcare are used for things like diagnosis, treatment planning, patient monitoring, and administrative decisions that affect patient care. For example, AI can help detect diseases such as sepsis early in intensive care units or assist in breast cancer screening. In the U.S., more than 1,200 medical devices with AI or machine learning have been approved by the Food and Drug Administration (FDA). This shows AI in medicine is growing fast and becoming more complex.
These technologies can improve how doctors diagnose and treat patients. But they also bring concerns about safety, privacy, fairness, and transparency. Sometimes, AI makes mistakes if it is trained on limited or biased data. This can cause problems for certain patient groups, raising questions about fairness in care.
Also, many AI systems operate as “black boxes.” This means the way they make decisions is not clear to doctors or patients. That makes it hard to trust AI or to get proper consent, especially when AI helps make important medical decisions. Because of this, health organizations and regulators want to make sure AI is safe, clear about how it works, and responsible.
The United States has many different rules about AI in healthcare. It does not have one big law like the European Union’s AI Act, which will start in 2026 and sets rules for high-risk AI. Instead, the U.S. uses existing laws adapted for AI. These include FDA rules for medical devices, the Health Insurance Portability and Accountability Act (HIPAA) for privacy, and various state laws.
The FDA approves medical devices that use AI. It uses a risk-based approach, meaning that the level of review depends on how much AI can affect patients’ safety or health. AI that diagnoses or manages treatment gets more careful review than AI used for office tasks or support.
The FDA has approved many AI and machine learning devices, but there are still challenges. Some AI systems learn and update themselves over time. These “adaptive” systems are hard to regulate with current rules. The FDA is working on new ways to handle these changing algorithms, but the rules are still being developed.
AI depends a lot on patient data. In the U.S., HIPAA protects patient health information mainly from entities like hospitals and insurers. But HIPAA does not fully cover all ways AI uses data, especially data used again for training AI models. This means privacy protection and patient consent can sometimes be unclear, especially in research and business uses of AI.
Often, patient permission is needed to use sensitive data. But managing consent for all stages of AI work—from gathering data to training and using AI—can be difficult. Healthcare administrators need to follow data rules carefully and use strong privacy protections.
One difficult question is who is responsible if AI causes harm. If AI gives a wrong diagnosis or treatment suggestion, is the AI developer, the healthcare provider, or the hospital to blame? Right now, the U.S. uses medical malpractice laws and product liability rules that were made before AI and don’t fully fit its features.
Some experts suggest using strict liability or no-fault compensation. This means holding developers or manufacturers responsible without needing to prove fault, similar to ideas in some European laws. Others think AI makers and users should have insurance to cover possible harm.
Clear liability rules are needed because many people are involved in AI decisions, like software engineers and clinicians. To manage risks well, there should be teamwork and clear records of AI use and human checks.
Apart from laws, ethical issues must guide how AI is used. AI can have different kinds of bias:
Bias can not only be unfair but also danger to patient safety. For example, some AI risk scores underestimate how sick Black patients are because they use healthcare cost as a proxy. This may lead to less care for those patients.
Healthcare groups should keep checking AI for bias. They must watch how AI works for different populations and update AI as medical knowledge changes.
Being clear and explaining how AI works is also important. Patients and doctors should know how AI affects care decisions. Though it is hard to explain AI completely because of its complexity, efforts are being made to make AI easier to understand. Good communication about AI builds patient control and trust.
To keep patients safe, people must review AI decisions carefully. There should be rules for when to get expert opinions or override AI, especially in serious cases like cancer or intensive care.
For healthcare managers and IT staff, AI helps with more than just medical diagnoses. AI can improve daily operations by automating front-office tasks, such as scheduling appointments, making patient intake calls, and following up on administrative duties. This reduces workload for staff and makes work smoother.
Some companies use AI for phone answering services. These systems manage appointments, send reminders, and answer general questions. This lets staff focus on harder tasks.
AI phone automation in healthcare makes services easier to reach any time, cuts wait times, and reduces mistakes in managing appointments. It also works well with electronic health records while protecting patient privacy under HIPAA.
AI can help with medical documentation by transcribing doctor-patient talks quickly and accurately. This saves doctors time and lowers errors in notes. It gives doctors more chance to focus on patients.
AI also helps with billing, insurance claims, and managing records. This makes administration run better and cuts costs. These benefits are very important for busy clinics or hospitals that have few staff and many patients.
For AI to work well, it must fit into current systems and routines. IT managers should check that AI tools connect easily with electronic health records, scheduling software, and follow privacy rules. They also need clear plans for human checks and backup if AI fails. This keeps safety and trust strong.
Using AI to automate workflows can save resources and make staff work better. This helps healthcare work better without risking patient safety or data privacy.
Rules and ethics for AI in U.S. healthcare are changing fast. Around the world, groups like the OECD and the World Health Organization are working on global AI standards. For example, the WHO launched the Smart AI Resource Assistant for Health (S.A.R.A.H.) in 2024 to help guide AI use.
In the U.S., the FDA is updating rules on AI that learns and changes over time. There are also efforts to clarify who is liable when AI causes harm. Healthcare organizations must watch privacy, bias, transparency, and human supervision carefully.
Healthcare managers, owners, and IT staff need to keep up with new rules and best practices. They have an important job in checking that AI tools are safe, follow the law, and improve operations while keeping patients safe.
Meeting ethical standards along with legal rules helps patients trust AI. This trust is very important if AI is going to be a regular tool in healthcare, as normal as the stethoscope.
This article combined information from research and laws to explain the challenges and rules for using high-risk AI systems safely in U.S. healthcare. With careful management and checks, AI can support health care innovation while protecting patients, doctors, and hospitals from risks.
AI improves healthcare by enhancing resource allocation, reducing costs, automating administrative tasks, improving diagnostic accuracy, enabling personalized treatments, and accelerating drug development, leading to more effective, accessible, and economically sustainable care.
AI automates and streamlines medical scribing by accurately transcribing physician-patient interactions, reducing documentation time, minimizing errors, and allowing healthcare providers to focus more on patient care and clinical decision-making.
Challenges include securing high-quality health data, legal and regulatory barriers, technical integration with clinical workflows, ensuring safety and trustworthiness, sustainable financing, overcoming organizational resistance, and managing ethical and social concerns.
The AI Act establishes requirements for high-risk AI systems in medicine, such as risk mitigation, data quality, transparency, and human oversight, aiming to ensure safe, trustworthy, and responsible AI development and deployment across the EU.
EHDS enables secure secondary use of electronic health data for research and AI algorithm training, fostering innovation while ensuring data protection, fairness, patient control, and equitable AI applications in healthcare across the EU.
The Directive classifies software including AI as a product, applying no-fault liability on manufacturers and ensuring victims can claim compensation for harm caused by defective AI products, enhancing patient safety and legal clarity.
Examples include early detection of sepsis in ICU using predictive algorithms, AI-powered breast cancer detection in mammography surpassing human accuracy, and AI optimizing patient scheduling and workflow automation.
Initiatives like AICare@EU focus on overcoming barriers to AI deployment, alongside funding calls (EU4Health), the SHAIPED project for AI model validation using EHDS data, and international cooperation with WHO, OECD, G7, and G20 for policy alignment.
AI accelerates drug discovery by identifying targets, optimizes drug design and dosing, assists clinical trials through patient stratification and simulations, enhances manufacturing quality control, and streamlines regulatory submissions and safety monitoring.
Trust is essential for acceptance and adoption of AI; it is fostered through transparent AI systems, clear regulations (AI Act), data protection measures (GDPR, EHDS), robust safety testing, human oversight, and effective legal frameworks protecting patients and providers.
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